2 citations · 3 across the 5 of their papers we have counts for
5 papers · 1 filter
A Unified Framework for Center-based Clustering of Distributed Data
Aleksandar Armacki, Dragana Bajović, Dušan Jakovetić +1
We develop a family of distributed center-based clustering algorithms that work over networks of users. In the proposed scenario, users contain a local dataset and communicate only…
High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
Aleksandar Armacki, Pranay Sharma, Gauri Joshi +3
We study high-probability convergence guarantees of learning on streaming data in the presence of heavy-tailed noise. In the proposed scenario, the model is updated in an online fa…
A One-shot Framework for Distributed Clustered Learning in Heterogeneous Environments
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments in which users obtain data from one of different distribut…
Personalized Federated Learning via Convex Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
We propose a parametric family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clust…
Gradient Based Clustering
Aleksandar Armacki, Dragana Bajovic, Dusan Jakovetic +1
We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluste…